I-flight estimation of unmeasurable turbofan engine outputs, such as thrust, is difficult because the values depend on the degradation level of the engine; which is often not known accurately. Degradation is generally defined in terms of off-nominal values of health parameters, such as efficiency and flow capacity, related to each major engine component. By the invention, it is now possible to estimate these health parameters deviations, given that there are at least as many sensors as parameters to be estimated. In standard engine installations, however, there are typically fewer sensors than health parameters, making accurate estimation impossible. An approach used in this situation is to select a subset of health parameters to estimate, assuming the others remain unchanged. If any of the unaccounted-for health parameters deviate from nominal, their effect will be captured to some extent in the estimated subset. As a result, the estimated values will no longer represent the true health parameters deviations. There are examples in the literature of a subset of health parameter “tuners” being used to reconstruct performance variables such as thrust (Luppold, R. H., Gallops, G. W., Kerr, L. J., Roman, J. R., 1989, “Estimating I-Flight Engine Performance Variations Using Kalman Filter Concepts,” AIAA-89-2584; Turevskiy, A., Meisner, R., Luppold, R. H., Kern, R. A., and Fuller, J. W., 2002, “A Model-Based Controller for Commercial Aero Gas Turbines,” ASME Paper GT2002-30041; Kobayashi, T., Simon, D. L., Litt, J. S., 2005, “Application of a Constant Gain Extended Kalman Filter for I-Flight Estimation of Aircraft Engine Performance Parameters,” ASME Paper GT2005-68494; the entire disclosures of which are herein incorporated by reference), but this approach of health parameter subset selection is much better established as a diagnostic tool for gas path analysis (Brotherton, T., Volponi A., Luppold, R., Simon, D. L., 2003, “eSTORM: Enhanced Self Tuning O-board Realtime Engine Model,” Proceedings of the 2003 IEEE Aerospace Conference; Kobayashi, T., Simon, D. L., 2003, “Application of a Bank of Kalman Filters for Aircraft Engine Fault Diagnostics,” ASME Paper GT2003-38550; Kobayashi, T., Simon, D. L., 2004, “Evaluation of an Enhanced Bank of Kalman Filters for I-Flight Aircraft Engine Sensor Fault Diagnostics,” ASME Paper GT2004-53640; the entire disclosures of which are herein incorporated by reference), where studies have determined which health parameters give good indications of certain faults for particular types of turbine engines (Stamatis, A., Mathioudakis, K., Papailiou, K. D., 1990, “Adaptive Simulation of Gas Turbine Performance,” Journal of Engineering for Gas Turbines and Power, 112, pp. 168-175; Tsalavoutas, A., Mathioudakis, K., Stamatis, A., Smith, M., 2001, “Identifying Faults in the Variable Geometry System of a Gas Turbine Compressor,” Journal of Turbomachinery, 123, pp. 33-39; Ogaji, S. O. T., Sampath, S., Singh, R., Probert, S. D., 2002, “Parameter Selection for Diagnosing a Gas-Turbine's Performance-Deterioration,” Applied Energy, 73, pp. 25-46; the entire disclosures of which are herein incorporated by reference).
When a Kalman filter is used to estimate the subset of health parameters, the estimates of measured outputs will usually be good, i.e., the sensed outputs and the recreated values obtained using the health parameter estimates will match, even if the health parameter estimates themselves are inaccurate. However, good estimation of sensed outputs does not guarantee that the estimation of unmeasured outputs will be accurate. Since thrust is affected by the level of degradation, poor health parameter estimation can result in poor thrust reconstruction. It might be possible to determine a subset of health parameters that produces good thrust reconstruction even when all health parameters deviate, but this is a time-consuming, empirical, trial-and-error process that gives no guarantee about the optimality of the result given the potential range of health parameter deviations and operating conditions.
The main issue that affects the estimation accuracy is that the total influence of the health parameters needs to be approximated using fewer variables. The selection of a subset of health parameters is not a general approach to solving this problem as long as all health parameters may deviate.